Zarządzanie Finansowe Archive

Former Cisco CEO John Chambers got it mostly right when he said that every company today is a technology company. In fact, every company is becoming a technology and data company, and the consequences of this distinction are substantial.
The real value of the Internet of Things (IoT) lies in the data it serves up and the insights that result. Much has been written about how IoT is unlocking significant value for companies by enabling smart factories and connected supply chains as well as the ability to monitor products and deliver new services. But IoT isn’t just changing how companies operate; it’s changing the very nature of their businesses. In asset-heavy industries, the proliferation of IoT data is fundamentally shifting the customer value proposition from goods to services, and this shift is leading companies to adopt new business models that require new capabilities.
The majority of IoT solutions today are built around internal applications such as predictive maintenance, factory optimization, supply chain automation, and improved product design. But to fully capture the value of their IoT data, B2B companies need to think beyond their own walls. By collaborating with new business partners, including industry incumbents and players in other sectors, companies can form new data ecosystems. These ecosystems give their participants access to valuable collective data assets as well as the capabilities and domain expertise necessary to develop the assets into new data-driven products and services.
Data ecosystems will play a critical role in defining the future of competition in many B2B industries. They enable companies to build data businesses, which are valuable not only because they generate high-margin recurring revenue streams but also because they create competitive advantage. New data-driven products and services deliver unique value propositions that extend beyond a company’s traditional hardware products, deepening customer relationships and raising barriers to entry. They also build highly defensible positions, thanks to natural monopolies rooted in economies of scale and scope (similar to monopolies based on proprietary IP or trade secrets). Companies that secure advantaged positions in data ecosystems will generate significant value and competitive advantage across their entire business, including their traditional hardware offerings.

Digital ecosystems—networks of companies, consumers, customers, and others that interact to create mutual value—have enabled some of the most profitable and valuable business models that exist today. (See “Getting Physical: The Rise of Hybrid Ecosystems,” BCG article, September 2017, and “The Age of Digital Ecosystems: Thriving in a World of Big Data,” BCG article, July 2013.) In fact, the five most valuable public companies in the US (at the time of publishing)—Apple, Google, Microsoft, Facebook, and Amazon—are all orchestrators of digital ecosystems. These digital leaders have built platform-based business models that capitalize on the winner-take-all dynamic of ecosystem competition to reach enormous scale and establish dominant positions.

These orchestrators exploit three factors:

They scale up rapidly, capitalizing on virtually zero marginal production costs, network effects, and low barriers to geographical expansion (in the absence of protectionism).

They take advantage of the “data flywheel effect”; digital ecosystems enable unprecedented data accumulation and analysis, fueling improvements to products and business processes and stimulating further growth and data access.

And ecosystems are able to provide seamless and comprehensive digital experiences for customers by organizing business partners on a single platform to satisfy multiple customer needs. They thereby lock in customers and capture a greater portion of their attention, time, and value.

Organizational culture can accelerate the application of analytics, amplify its power, and steer companies away from risky outcomes. Here are seven principles that underpin a healthy data culture.

Revolutions, it’s been remarked, never go backward. Nor do they advance at a constant rate. Consider the immense transformation unleashed by data analytics. By now, it’s clear the data revolution is changing businesses and industries in profound and unalterable ways.

But the changes are neither uniform nor linear, and companies’ data-analytics efforts are all over the map. McKinsey research suggests that the gap between leaders and laggards in adopting analytics, within and among industry sectors, is growing. We’re seeing the same thing on the ground. Some companies are doing amazing things; some are still struggling with the basics; and some are feeling downright overwhelmed, with executives and members of the rank and file questioning the return on data initiatives.

For leading and lagging companies alike, the emergence of data analytics as an omnipresent reality of modern organizational life means that a healthy data culture is becoming increasingly important. With that in mind, we’ve spent the past few months talking with analytics leaders at companies from a wide range of industries and geographies, drilling down on the organizing principles, motivations, and approaches that undergird their data efforts. We’re struck by themes that recur over and again, including the benefits of data, and the risks; the skepticism from employees before they buy in, and the excitement once they do; the need for flexibility, and the insistence on common frameworks and tools. And, especially: the competitive advantage unleashed by a culture that brings data talent, tools, and decision making together.
The experience of these leaders, and our own, suggests that you can’t import data culture and you can’t impose it. Most of all, you can’t segregate it. You develop a data culture by moving beyond specialists and skunkworks, with the goal of achieving deep business engagement, creating employee pull, and cultivating a sense of purpose, so that data can support your operations instead of the other way around.

In this article, we present seven of the most prominent takeaways from conversations we’ve had with these and other executives who are at the data-culture fore. None of these leaders thinks they’ve got data culture “solved,” nor do they think that there’s a finish line. But they do convey a palpable sense of momentum. When you make progress on data culture, they tell us, you’ll strengthen the nuts and bolts of your analytics enterprise.

That will not only advance your data revolution even further but can also help you avoid the pitfalls that often trip up analytics efforts. We’ve described these at length in another article and have included, with three of the seven takeaways here, short sidebars on related “red flags” whose presence suggests you may be in trouble—along with rapid responses that can mitigate these issues. Taken together, we hope the ideas presented here will inspire you to build a culture that clarifies the purpose, enhances the effectiveness, and increases the speed of your analytics efforts.

Companies new to the space can learn a great deal from early adopters who have invested billions into AI and are now beginning to reap a range of benefits.

After decades of extravagant promises and frustrating disappointments, artificial intelligence (AI) is finally starting to deliver real-life benefits to early-adopting companies. Retailers on the digital frontier rely on AI-powered robots to run their warehouses—and even to automatically order stock when inventory runs low. Utilities use AI to forecast electricity demand. Automakers harness the technology in self-driving cars.

A confluence of developments is driving this new wave of AI development. Computer power is growing, algorithms and AI models are becoming more sophisticated, and, perhaps most important of all, the world is generating once-unimaginable volumes of the fuel that powers AI—data. Billions of gigabytes every day, collected by networked devices ranging from web browsers to turbine sensors.

The entrepreneurial activity unleashed by these developments drew three times as much investment in 2016—between $26 billion and $39 billion—as it did three years earlier. Most of the investment in AI consists of internal R&D spending by large, cash-rich digital-native companies like Amazon, Baidu, and Google.

For all of that investment, much of the AI adoption outside of the tech sector is at an early, experimental stage. Few firms have deployed it at scale. In a McKinsey Global Institute discussion paper, Artificial intelligence: The next digital frontier?, which includes a survey of more than 3,000 AI-aware companies around the world, we find early AI adopters tend to be closer to the digital frontier, are among the larger firms within sectors, deploy AI across the technology groups, use AI in the most core part of the value chain, adopt AI to increase revenue as well as reduce costs, and have the full support of the executive leadership. Companies that have not yet adopted AI technology at scale or in a core part of their business are unsure of a business case for AI or of the returns they can expect on an AI investment.

However, early evidence suggests that there is a business case to be made, and that AI can deliver real value to companies willing to use it across operations and within their core functions. In our survey, early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and expect the performance gap with other firms to widen in the next three years.

This adoption pattern is widening a gap between digitized early adopters and others. Sectors at the top of MGI’s Industry Digitization Index, such as high tech and telecoms or financial services, are also leading AI adopters and have the most ambitious AI investment plans. These leaders use multiple technologies across multiple functions or deploy AI at the core of their business. Automakers, for example, use AI to improve their operations as well as develop self-driving vehicles, while financial-services companies use it in customer-experience functions. As these firms expand AI adoption and acquire more data, laggards will find it harder to catch up.

Governments also must get ahead of this change, by adopting regulations to encourage fairness without inhibiting innovation and proactively identifying the jobs that are most likely to be automated and ensuring that retraining programs are available to people whose livelihoods are at risk from AI-powered automation. These individuals need to acquire skills that work with, not compete against, machines.

The future of AI will be innovative, but may not be shared equally. Companies based in the United States absorbed 66 percent of all external investments into AI companies in 2016, according to our global review; China was second, at 17 percent, and is growing fast. Both countries have grown AI “ecosystems”—clusters of entrepreneurs, financiers, and AI users—and have issued national strategic plans in the past 18 months with significant AI dimensions, in some cases backed up by billions of dollars of AI-funding initiatives. South Korea and the United Kingdom have issued similar strategic plans. Other countries that desire to become significant players in AI would be wise to emulate these leaders.

Significant gains are there for the taking. For many companies, this means accelerating the digital-transformation journey. AI is not going to allow companies to leapfrog getting the digital basics right. They will have to get the right digital assets and skills in place to be able to effectively deploy AI.

ARTIFICIAL INTELLIGENCE

Artificial intelligence is poised to unleash the next wave of digital disruption, and companies should prepare for it now. We already see real-life benefits for a few earlyadopting firms, making it more urgent than ever for others to accelerate their digital transformations. Our findings focus on five AI technology systems: robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning, which includes deep learning and underpins many recent advances in the other AI technologies. AI investment is growing fast, dominated by digital giants such as Google and Baidu. Globally, we estimate tech giants spent $20 billion to $30 billion on AI in 2016, with 90 percent of this spent on R&D and deployment, and 10 percent on AI acquisitions. VC and PE financing, grants, and seed investments also grew rapidly, albeit from a small base, to a combined total of $6 billion to $9 billion. Machine learning, as an enabling technology, received the largest share of both internal and external investment. AI adoption outside of the tech sector is at an early, often experimental stage. Few firms have deployed it at scale. In our survey of 3,000 AI-aware C-level executives, across 10 countries and 14 sectors, only 20 percent said they currently use any AIrelated technology at scale or in a core part of their businesses. Many firms say they are uncertain of the business case or return on investment. A review of more than 160 use cases shows that AI was deployed commercially in only 12 percent of cases.

Adoption patterns illustrate a growing gap between digitized early AI adopters and others. Sectors at the top of MGI’s Industry Digitization Index, such as high tech and telecom or financial services, are also leading adopters of AI. They also have the most aggressive AI investment intentions. Leaders’ adoption is both broad and deep: using multiple technologies across multiple functions, with deployment at the core of their business. Automakers use AI to develop self-driving vehicles and improve operations, for example, while financial services firms are more likely to use it in customer experience–related functions. Early evidence suggests that AI can deliver real value to serious adopters and can be a powerful force for disruption. In our survey, early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and expect the performance gap with other firms to widen in the future. Our case studies in retail, electric utilities, manufacturing, health care, and education highlight AI’s potential to improve forecasting and sourcing, optimize and automate operations, develop targeted marketing and pricing, and enhance the user experience.

AI’s dependence on a digital foundation and the fact that it often must be trained on unique data mean that there are no shortcuts for firms. Companies cannot delay advancing their digital journeys, including AI. Early adopters are already creating competitive advantages, and the gap with the laggards looks set to grow. A successful program requires firms to address many elements of a digital and analytics transformation: identify the business case, set up the right data ecosystem, build or buy appropriate AI tools, and adapt workflow processes, capabilities, and culture. In particular, our survey shows that leadership from the top, management and technical capabilities, and seamless data access are key enablers. . AI promises benefits, but also poses urgent challenges that cut across firms, developers, government, and workers. The workforce needs to be reskilled to exploit AI rather than compete with it; and countries serious about establishing themselves as a global hub for AI development will need to join the global competition to attract AI talent and investment; and progress will need to be made on the ethical, legal and regulatory challenges that could otherwise hold back AI.